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Abstract:
Bats are able to employ an astonishin-
gly complex vocal repertoire for navigating their
environment and conveying social information. A
handful of species also show evidence for vocal
learning, an extremely rare ability shared only
with humans and few other animals. However,
despite their obvious potential for the study
of vocal communication, bats remain severely
understudied at a molecular level. To address
this fundamental gap we performed the first
transcriptome profiling and genetic interrogation
of molecular networks in the brain of a highly vo-
cal bat species, P. discolor. To identify functional,
biologically relevant gene networks, we utilized
two contrasting co-expression network analysis
methods with distinct underlying algorithms;
WGCNA and MCLUST. These methods typically
need large sample sizes for correct clustering,
which can be prohibitive where samples are
limited, such as in this study. To overcome this,
we built on the WGCNA and MCLUST methods to
develop a novel approach for identifying robust
co-expression gene networks using few samples
(≤6). Using this approach, we were able to ge-
nerate tissue-specific functional gene networks
from the bat PAG, a brain region fundamental
for mammalian vocalization. The most highly
connected of the networks identified in our study
represented a cluster of genes involved in glu-
tamatergic synaptic transmission. Glutamatergic
signaling plays an essential role in vocalizations
elicited from the PAG, suggesting that the gene
network uncovered here is mechanistically impor
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tant for vocal-motor control in mammals. These
findings show that our innovative gene clustering
approach can reveal robust biologically relevant
gene co-expression networks with limited sample
sizes. Moreover, this work reports the first gene
network analysis performed in a bat brain and
establishes P. discolor as a novel, tractable model
system for understanding the genetics of vocal
communication.
